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When it is required to sort the palindrome words present in a sentence, a method is defined that takes a string as a parameter and first ensures that it is a palindrome. Then it sorts all the words of a string and returns it as output.ExampleBelow is a demonstration of the samedef check_palindrome(my_string): if(my_string == my_string[::-1]): return True else: return False def print_sort_palindromes(my_sentence): my_list = [] my_result = list(my_sentence.split()) for ... Read More
When it is required to generate all possible permutations of a word in a sentence, a function is defined. This function iterates over the string and depending on the condition, the output is displayed.ExampleBelow is a demonstration of the samefrom itertools import permutations def calculate_permutations(my_string): my_list = list(my_string.split()) permutes = permutations(my_list) for i in permutes: permute_list = list(i) for j in permute_list: print j print() my_string = "hi there" print("The string is :") print(my_string) ... Read More
Use index=False to ignore index. Let us first import the required library −import pandas as pdCreate a DataFrame −dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35]], index=['x', 'y', 'z'], columns=['a', 'b'])Select rows by passing label using loc −dataFrame.loc['x'] Display DataFrame without index −dataFrame.to_string(index=False)ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35]], index=['x', 'y', 'z'], columns=['a', 'b']) # DataFrame print"Displaying DataFrame with index...", dataFrame # select rows with loc print"Select rows by passing label..." print(dataFrame.loc['x']) # display DataFrame without index print"Displaying DataFrame without Index...", dataFrame.to_string(index=False)OutputThis will produce ... Read More
When it is required to extract rows from a matrix with different data types, it is iterated over and ‘set’ is used to get the distinct types.ExampleBelow is a demonstration of the samemy_list = [[4, 2, 6], ["python", 2, {6: 2}], [3, 1, "fun"], [9, (4, 3)]] print("The list is :") print(my_list) my_result = [] for sub in my_list: type_size = len(list(set([type(ele) for ele in sub]))) if len(sub) == type_size: my_result.append(sub) print("The resultant distinct data type rows are :") print(my_result)OutputThe list is : [[4, ... Read More
When it is required to split the list into all the possible tuple pairs, a method can be defined that takes a list as a parameter and uses list comprehension to iterate through the list and use ‘extend’ methodExampleBelow is a demonstration of the samedef determine_pairings(my_list): if len(my_list)
When it is required to reverse a given range in a list, it is iterated over and the ‘:’ operator along with slicing is used to reverse it.ExampleBelow is a demonstration of the samemy_list = ["Hi", "there", "how", 'are', 'you'] print("The list is : ") print(my_list) m, n = 2, 4 my_result = [] for elem in my_list: my_result.append(elem[m : n + 1]) print("The sliced strings are : " ) print(my_result)OutputThe list is : ['Hi', 'there', 'how', 'are', 'you'] The sliced strings are : ['', 'ere', 'w', 'e', 'u']ExplanationA list is defined, and ... Read More
When it is required to find the cumulative mean of the dictionary keys, an empty dictionary is created, and the original dictionary is iterated over, and the items are accessed. If this is present in the dictionary, the key is appended to the empty dictionary, otherwise the value is placed instead of the key.ExampleBelow is a demonstration of the samefrom statistics import mean my_list = [{'hi' : 24, 'there' : 81, 'how' : 11}, {'hi' : 16, 'how' : 78, 'doing' : 63}] print("The list is : ") print(my_list) my_result = dict() for sub ... Read More
For mean, use the mean() function. Calculate the mean for the column with NaN and use the fillna() to fill the NaN values with the mean.Let us first import the required libraries −import pandas as pd import numpy as npCreate a DataFrame with 2 columns and some NaN values. We have entered these NaN values using numpy np.NaN −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Lexus', 'Mustang', 'Bentley', 'Mustang'], "Units": [100, 150, np.NaN, 80, np.NaN, np.NaN] } )Finding mean of the column values with NaN i.e, for Units columns here. So, the Units ... Read More
When it is required to increment the last element by 1 when a decimal value is given an input, a method named ‘increment_num’ is defined that checks to see if the last element in the list is less than 9. Depending on this, operations are performed on the elements of the list.ExampleBelow is a demonstration of the samedef increment_num(my_list, n): i = n if(my_list[i] < 9): my_list[i] = my_list[i] + 1 return my_list[i] = 0 ... Read More
To merge Pandas DataFrame, use the merge() function. The left outer join is implemented on both the DataFrames by setting under the “how” parameter of the merge() function i.e. −how = “left”At first, let us import the pandas library with an alias −import pandas as pd Let’s create two DataFrames to be merged −# Create DataFrame1 dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } ) # Create DataFrame2 dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], ... Read More